Traditionally, human heartbeat was thought to be regulated according to classical principles of homeostasis. Under this theory, the human physiological system operates in a manner which adjusts heart rate variability to achieve a state of equilibrium. Clinicians, in fact, traditionally described the normal beat activity of the heart as a "regular or normal sinus rhythm."
Modern views now depart from these traditional ideologies. More recent studies and research show that, even with healthy individuals, the heart does not beat with metronomic regularity. Rather, the heart exhibits beat-to-beat fluctuations which are far from equilibrium. See C. K. Peng, et al, "Fractal Landscapes in Physiology & Medicine: Long-Range Correlations in DNA Sequences and Heart Rate Intervals" pp. 55-65, appearing in Fractals in Biology and Medicine, by T. F. Nonnenmacher, et al (Ed.) (1994). Electrocardiograms, for example, show that an individual will exhibit a fluctuating or erratic heart rate variability during both rest and sleep periods.
Beat-to-beat fluctuations which occur around a person's mean heart rate are known as heart rate variability. The fluctuations from beat-to-beat are attributed, in part, to the nonlinear interaction between the sympathetic and parasympathetic branches of the involuntary nervous system. The sympathetic autonomic and parasympathetic autonomic nervous systems regulate, to some extent, the sinoatrial (SA) node and atrioventricular (AV) node of the heart and, thus, largely influence the control of the heart rate. These two nervous systems operate somewhat reciprocally to effect changes in the heart rate. In this regard, parasympathetic stimulation decreases the firing rate of the pacing cells located in the sinus node of the heart. Sympathetic stimulation, on the other hand, increases this firing rate.
Most clinicians agree that the parasympathetic and sympathetic inputs to the SA node mediate low frequency heart rate fluctuations (i.e., generally below 0.15 Hz), whereas modulation of parasympathetic outflow mediates higher frequency fluctuations. Studies have further shown that a decrease in heart rate variability correlates with a decrease in parasympathetic nervous activity and an accompanied increase in sympathetic nervous activity. See J. Thomas Bigger, et. al, "Components of Heart Rate Variability Measured During Healing of Acute Myocardial Infarction" American Journal of Cardiology, Vol. 61 (1988), pp.208-215. In a healthy, resting heart, for instance, the parasympathetic activity dominates to maintain the heart rate. However, in an unhealthy heart, for example one having heart disease, sympathetic activity may more influence and control the heart rate.
Over the past several years, heart rate variability was increasingly recognized as a diagnostic and a prognostic indication of the cardiac health risks to which a person is susceptible. As a result, much research has been directed toward heart rate variability. In particular, clinicians have been investigating the possibility that heart rate variability may provide important information to forecast impending cardiac anomalies. One study, for example, verified that a low standard deviation of heart rate variability is a powerful prognostic indicator of sudden coronary death in patients recovering from acute myocardial infarction. See Alberto Malliani, et. al, "Power Spectral Analysis of Cardiovascular Variability in Patients at Risk for Sudden Cardiac Death" Journal of Cardiovascular Electrophysiology, Vol. 5 (1994), pp. 274-286.
Today, cardiologists generally are in accord that heart rate variability does have a correlation to the present condition of a person's heart rate or the future occurrence of an abnormal cardiac event. In fact, numerous studies have been performed which demonstrate this correlation. For example, if the heart rate of a healthy individual is compared to the rate of a patient having congestive heart failure, distinct differences in the beat intervals will be observed. In this regard, the healthy individual will exhibit more complex patterns of fluctuation than the non-healthy individual.
Furthermore, studies specifically relate heart rate variability to death in cardiac patients. Diminished heart rate variability now is associated with an increased risk for ventricular fibrillation and sudden cardiac death. One study concluded:
Heart rate variability is an independent predictor of death when other known postinfarction risk variables (for example, prolonged left ventricular ejection fraction, ventricular arrhythmias, and clinical variables) are considered. Heart rate variability has a higher association with risk for death than other variables obtained by Holter monitoring, (for example, mean heart rate and ventricular arrhythmias). Heart rate variability also appears to be a better predictor of arrhythmia complications than prolongation of the ejection fraction.
See Conny M. A. van Ravenswaaij-Arts, et. al, Annals of Internal Medicine, Vol. 118 (1993), pp. 436-447.
As noted, clinicians use heart rate variability to predict the onset of sudden cardiac death. Although the exact cause of cardiac death is not completely understood, most victims suffer from ventricular tachycardia that degenerates into ventricular fibrillation. Investigators have exhausted significant effort to predict the onset and triggers for such ventricular tachyarrhythmias. Heart rate variability is one available predictive value. Recent studies in this field verify that a decrease or increase in heart rate variability during the first several weeks after an acute myocardial infarction may be used to predict subsequent mortality or ventricular rhythmic disorders. One study examined approximately 800 patients who had survived an acute myocardial infarction and concluded that patients with a heart rate variability of less than 50 milliseconds had a 5.3 times higher mortality rate than those patients with a heart rate variability of more than 100 milliseconds. See Robert E. Kleiger, et. al, "Decreased Heart Rate Variability and Its Association with Increased Mortality After Acute Myocardial Infarction" American Journal of Cardiology, Vol. 59 (1987), pp. 256-262. Patients experiencing congestive heart failure and coronary artery disease also exhibit a decrease in heart rate variability. See Casolo G. et al, "Decreased Spontaneous Heart Rate Variability in Congestive Heart Failure," American Journal of Cardiology, Vol. 64 (1989), pp. 1162-1167.
Even in healthy individuals having normal heart rate variability, the heart rate intervals generally have a circadian variation. This circadian variation, however, may begin to become less pronounced and more irregular several minutes to several hours before the onset of an abnormal cardiac event. Researchers, for example, have found that heart rate variability progressively decreases in the hours preceding the onset of arrhythmia. Monitoring heart rate variability in such instances thus provides clinicians with a tool to forecast impending cardiac events.
As one advantage, measurements of heart rate variability are generally non-invasive and may be reliably performed. A Holter monitor or electrodes affixed to the patient measure heart rate very accurately. The electrodes detect the heartbeat, usually the R-R interval, for a series of beats. Thereafter, statistical data, such as mean, median, and standard deviation, are computed and then used to forecast the onset of a cardiac event. One known method for using heart rate variability is to compare heart rate intervals recorded under normal heart rate conditions to subsequent heart rate intervals. Deviations between the two recordings then may be used as an indication of heart rate variability fluctuation. In one embodiment, a Holter monitor records R-R intervals while the patient exhibits normal or healthy heart rate variability. An algorithm based on mean and standard deviation then computes a single user value which is stored in permanent memory. This user value represents the patient's stress state during normal heart rate variability conditions. Thereafter, the patient wears a wrist detector which monitors the R-R intervals for discrete beat periods, for example 100 beats. Once a beat period is complete, the wrist detector uses the algorithm to compute the patient's present user value or present stress state. This present user value is then compared to the permanently stored user value which was previously recorded under normal heart rate conditions. Theoretically, this comparison reveals deviations from normal heart rate variability which, in turn, are a measure of the patient's cardiac stress state. Large deviations between the two user values reflect large deviations in the autonomic nervous system balance between the sympathetic and parasympathetic activities. For example, if the presently recorded user value deviates from the permanently stored user value more than 25%, the patient may be subject to an elevated stress level with an accompanying abnormal heart rate variability.
One important disadvantage associated with methods and apparatus for utilizing heart rate variability concerns the failure to provide a more intelligent algorithmic structure. Heart rate variability algorithms typically first compute a present user value based on the R-R intervals. Thereafter, this present user value is compared with a previously stored user value and a deviation between the two is computed. The algorithmic structure itself, however, remains unchanged. Thus, when subsequent R-R intervals are received and new user values calculated, these values are again compared with the same permanently stored user value. As such, the algorithm repeatedly uses the same threshold parameters defining normal and abnormal heart rate variability.
Another disadvantage associated with methods and apparatus for utilizing heart rate variability concerns the treatment of heart rate variability data leading up to an abnormal cardiac event. Devices measuring heart rate variability often have memories which operate on a first-in-first-out basis. These types of memories hold the heart rate data in sequence and discard the oldest data and save the newest, incoming data. The older data, however, may provide important information regarding the onset of subsequent cardiac events.